In this section, we report the results of our experiments. Following standard practice, the temporal mechanism is inserted after the first convolution of each ResNet/MobileNet block. Furthermore, all backbones are initialized with ImageNet [1]} pre-trained weights. We performed all our experiments on the Something-Something V1 (SSv1) [2]} dataset, a standard video action recognition benchmark. Example video sequences from the benchmark dataset and their corresponding labels are shown in Fig REF . Throughout the experiments, we uniformly sampled 8 frames from the video and use them as input to the model and use a 2D-ResNet18 backbone unless stated otherwise. We also report the number of Giga Multiply-Accumulate operations (GMACs) per video (GMACs/video) as a measure of model efficiency, and the top-1 and top-5 accuracy (Top-1 Acc and Top-5 acc) as a measure of the model's performance.
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